欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
针对粒子群算法在寻优时容易陷入局部最优的不足，提出了一种基于子区域粒子群的算法，并运用到电力系统无功优化中。该算法将搜索空间划分成若干个子区域，在各个子区域中均使用粒子群算法进行寻优，通过比较各个子区域的全局最优解，得出整个搜索空间的全局最优。结合无功优化的数学模型应用-I-IEEE30节点之中，并与标准粒子群算法以及自适应变异粒子群算法的结果相比较，结果表明基于子区域粒子群算法能够大大地降低在寻优过程中陷入局部最优的概率，寻找出更好的全局最优解，在电力系统无功优化中得到良好的应用。
英文摘要：
A sub-region particle swarm optimization is proposed to overcome the fault of falling into local optimum of the particle swarm optimization （PSO）. It is applied to the reactive power optimization of power system. The proposed algorithm divides the search space into several sub-regions, and obtains the optimum of each region using PSO. Through comparing these sub-region optimums, the global optimum of the entire search space can be obtained. The sub-region particle swarm optimization is applied in IEEE30-bus system combining with the mathematical model of reactive power optimization. Through the comparison of PSO, adaptive mutation PSO and sub-region PSO in the reactive power optimization, it is found that the sub-region particle swarm optimization can reduce the probability of failing into local optimum, and easily find out the global optimum. This algorithm is suitable for the reactive power optimization of power system.
同期刊论文项目
同项目期刊论文
A New Classification Method for Transient Power Quality Combining Spectral Kurtosis with Neural Netw
期刊信息
位置：